A DSS for the buffer allocation of production lines based on a comparative evaluation of a set of search algorithms

Abstract

In the design of production lines, the classical approach to the buffer allocation problem (BAP) is to use a search algorithm in association with an evaluative algorithm to obtain the mathematical optimum of the specified objective function. In practice, a choice often has to be made regarding which search algorithm to use for the efficient solution of the BAP. This paper gives the results of a carefully selected set of experiments on short (K = number of stations = 3, 4,, 11 stations), medium length (K = 12, 13,, 30 stations) and long lines (K = 40, 50, , 100 stations) and, within each line, small N (N = total number of buffer slots = K/2 if K is even; = (K – 1)/2 if K is odd), medium N (N = K + 1) and large N (N = 2K) to evaluate the effectiveness of the following five search algorithms: simulated annealing, genetic, tabu search, myopic and complete enumeration (where possible). The production lines are balanced and the single exponential machine at each station is perfectly reliable. All the experiments were run on a readily available desktop PC with the following specifications: Windows XP Professional Version 2002 Service Pack 3, Pentium® Dual-Core CPU E5300@2.60 GHz, 2.00 GB RAM. The measures of performance used are CPU time required and closeness to the maximum throughput achieved. The five search algorithms are ranked in respect to these two measures and certain findings regarding their performance over the experimental set are noted. The distributions of buffer slots to storage areas for the algorithm(s) leading to maximum throughput are examined and certain patterns are found, leading to indications for design rules. Based on the results of the above experiments, two additional sets of experiments were carried out, one using the simulated annealing algorithm for production lines of K = 3 to 20 and N = 1 to 20 (accounting for a total number of 360 different production lines) and another using the myopic algorithm for production lines of K = 3 to 80 and N = 1 to 120 (accounting for a total number of 9360 different production lines). These results may be used as references for comparison purposes in the international literature. Using the results from all sets of experiments, a decision support system (DSS) is designed and implemented which, as is illustrated, may assist production line designers in making decisions regarding the most appropriate of the five search algorithms tested to use for the BAP-A (the dual problem) and the BAP-B (the primal problem) for a wide class of production lines (consisting of K = 3 to 80 and N = 1 to 120).